JACIII Vol.12 No.4 pp. 377-381
doi: 10.20965/jaciii.2008.p0377


Effect of Genetic Encoding on Evolution of Efficient Neural Controllers

Genci Capi

Graduate School of Science and Engineering for Research, University of Toyama, Gofuku Campus, 3190 Gofuku, Toyama 930-8555, Japan

April 23, 2007
June 29, 2007
July 20, 2008
multiobjective optimization, evolution, neural controllers
In this paper, we present a new method based on multiobjective evolutionary algorithms to evolve low complexity neural controllers for the robots that have to perform two different tasks, simultaneously. In our method, each task and the structure of neural controller are considered as separated objective functions. We compare the results of two different encoding schemes: (1) Connectionist encoding and (2) Node based encoding. Simulation results show that multiobjective evolution can be successfully applied to generate low complexity neural controllers. In addition, node based encoding outperformed connectionist encoding in terms of robot performance and robustness of the neural controller.
Cite this article as:
G. Capi, “Effect of Genetic Encoding on Evolution of Efficient Neural Controllers,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.4, pp. 377-381, 2008.
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